diff --git a/openseek/competition/LongContext-ICL-Annotation/READMD_cn.md b/openseek/competition/LongContext-ICL-Annotation/READMD_cn.md
new file mode 100644
index 0000000..5c593f5
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/READMD_cn.md
@@ -0,0 +1,73 @@
+# 超长长上下文场景中LLM自动数据标注挑战赛
+
+---
+
+## 消息
+<!-- BEGIN NEWS -->
+- **[2026-01-20] `发布`：** 赛事信息已在 **Kaggle** 正式上线。详情见：[FlagOS Open Computing Global Challenge](https://www.kaggle.com/competitions/flag-os-open-computing-global-challenge).
+- **[2026-01-06] `发布`：** 由 **众智 FlagOS 社区**、**北京智源人工智能研究院（BAAI）** 与 **CCF ODTC** 联合主办的综合性大赛 **FlagOS 开放计算全球挑战赛** 正式发布。详情见：  
+  [FlagOS开放计算全球挑战赛- AI赛事通 | 数据算法赛](https://www.competehub.dev/zh/competitions/modelscope180)
+<!-- END NEWS -->
+
+---
+
+
+## 快速开始
+### 1. 环境
+
+```bash
+openai
+torch
+flagScale
+```
+
+推荐在NVIDIA平台使用 `cd src && bash create_env_nvidia.sh` 创建环境。
+
+### 2. 下载模型权重
+```bash
+hf download Qwen/Qwen3-4B --local-dir Qwen3-4B
+# or
+modelscope download --model Qwen/Qwen3-4B 
+```
+### 3. 长文本配置
+在`Qwen3-4B/config.json`将原有配置替换为：
+```json
+"rope_scaling": {
+    "rope_type": "yarn",
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768
+}
+```
+### 4. 模型部署
+
+请根据实际需求，配置 `llm_config.yaml` 文件。启动配置  
+
+```bash
+cd FlagScale
+python run.py --config-path .. --config-name llm_config action=run
+```
+
+在模型服务启动后，可通过以下方式测试本地 API：
+
+```bash
+python api_test.py
+```
+
+如需停止服务，请执行：
+
+```bash
+python run.py --config-path .. --config-name llm_config action=stop
+```
+
+### 5. 运行/改进基线方法（Baseline）
+
+启动如下命令开始模型标注
+```bash
+python main.py
+```
+
+实现新的标注方法，请修改`method.py`文件。你可以在该文件中：  
+* 定义新的指令模板、
+* 定义新的上下文示例选择策略
+* 定义新的模型推理、标注方案
+* 添加自定义后处理逻辑
diff --git a/openseek/competition/LongContext-ICL-Annotation/READMD_zh_cn.md b/openseek/competition/LongContext-ICL-Annotation/READMD_zh_cn.md
deleted file mode 100644
index 82c61fb..0000000
--- a/openseek/competition/LongContext-ICL-Annotation/READMD_zh_cn.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# 超长长上下文场景中LLM自动数据标注挑战赛
-
----
-
-## 消息
-<!-- BEGIN NEWS -->
-- **[2026-01-20] `发布`：** 赛事信息已在 **Kaggle** 正式上线。详情见：[FlagOS Open Computing Global Challenge](https://www.kaggle.com/competitions/flag-os-open-computing-global-challenge).
-- **[2026-01-06] `发布`：** 由 **众智 FlagOS 社区**、**北京智源人工智能研究院（BAAI）** 与 **CCF ODTC** 联合主办的综合性大赛 **FlagOS 开放计算全球挑战赛** 正式发布。详情见：  
-  [FlagOS开放计算全球挑战赛- AI赛事通 | 数据算法赛](https://www.competehub.dev/zh/competitions/modelscope180)
-<!-- END NEWS -->
-
----
-
-## 简介
-长上下文场景中LLM自动数据标注挑战赛基于Qwen3-4B大语言模型，采用上下文（In-context Learning, ICL）范式开展自动化数据标注任务研究。参赛团队必须使用组委会统一提供的数据集，围绕超长上下文场景设计有效的 ICL 标注方案，并在统一评测集上完成推理与评测。组委会将依据标准化评测结果，对参赛方案进行综合评估并确定最终排名。
-
-### 赛题目标
-本赛题以大语言模型（Large Language Models，LLMs）为核心驱动力，面向超长上下文条件下的自动化数据标注问题，探索兼具效率与精度的新型技术范式。赛题重点聚焦以下三个关键科学与工程问题：
-1. 在超长上下文场景下，如何设计有效的模型指令与提示策略，引导 LLM 稳定、高质量地完成数据标注任务？
-2. 当可用标注示例数量显著超过模型上下文容量时，如何为待标注数据构造信息密集、结构合理的超长上下文输入？
-3. 在自动多轮对话或持续交互场景中，如何高效利用超长上下文，实现一致性与可扩展性兼顾的数据标注？
-
-### 赛题详情
-- 参赛团队自主设计一套完整的LLM自动数据标注方案，并在统一的数据集与评测设置下进行实验验证。比赛将以标准化榜单形式公布各参赛方案的评测分数及排名。
-- 除评测结果外，参赛团队还需按照赛事要求提交技术方案文档与可复现源代码。组委会将对提交方案进行复现验证，并对技术方案本身进行评审。最终成绩将由预测结果成绩与技术方案成绩加权计算得出，具体规则如下。
-- 参赛队伍需按照赛题和赛制要求，提交技术方案和完整代码至Github OpenSeek官方开源项目下。
-- 更多具体细节请参考[赛事平台](https://flagos.io/RaceDetail?id=296fmsd8&lang=cn)。
-- 关于赛事信息，一切以赛事平台公布信息为准。
-
----
-
-## 快速开始
-### 1. 环境
-
-```bash
-openai
-torch
-flagScale
-```
-
-推荐在NVIDIA平台使用 `cd src && bash create_env_nvidia.sh` 创建环境。
-
-### 2. 下载模型权重
-```bash
-hf download Qwen/Qwen3-4B --local-dir Qwen3-4B
-# or
-modelscope download --model Qwen/Qwen3-4B 
-```
-### 3. 长文本配置
-在`Qwen3-4B/config.json`将原有配置替换为：
-```json
-"rope_scaling": {
-    "rope_type": "yarn",
-    "factor": 4.0,
-    "original_max_position_embeddings": 32768
-}
-```
-### 4. 模型部署
-
-请根据实际需求，配置 `llm_config.yaml` 文件。启动配置  
-
-```bash
-cd FlagScale
-python run.py --config-path .. --config-name llm_config action=run
-```
-
-在模型服务启动后，可通过以下方式测试本地 API：
-
-```bash
-python api_test.py
-```
-
-如需停止服务，请执行：
-
-```bash
-python run.py --config-path .. --config-name llm_config action=stop
-```
-
-### 5. 运行/改进基线方法（Baseline）
-
-启动如下命令开始模型标注
-```bash
-python main.py
-```
-
-实现新的标注方法，请修改`method.py`文件。你可以在该文件中：  
-* 定义新的指令模板、
-* 定义新的上下文示例选择策略
-* 定义新的模型推理、标注方案
-* 添加自定义后处理逻辑
diff --git a/openseek/competition/LongContext-ICL-Annotation/README.md b/openseek/competition/LongContext-ICL-Annotation/README.md
index 25d1082..462d6cd 100644
--- a/openseek/competition/LongContext-ICL-Annotation/README.md
+++ b/openseek/competition/LongContext-ICL-Annotation/README.md
@@ -2,6 +2,8 @@
 
 Large Language Models Automatic Data Annotation under Long-Context Scenarios.
 
+---
+
 ## News
 <!-- BEGIN NEWS -->
 - **[2026-01-20] `Release`:** The competition is now officially live on **Kaggle**. See details: [FlagOS Open Computing Global Challenge](https://www.kaggle.com/competitions/flag-os-open-computing-global-challenge).
@@ -9,35 +11,7 @@ Large Language Models Automatic Data Annotation under Long-Context Scenarios.
   [FlagOS开放计算全球挑战赛- AI赛事通 | 数据算法赛](https://www.competehub.dev/zh/competitions/modelscope180)
 <!-- END NEWS -->
 
-## Introduction
-
-The LongContext-ICL-Annotation Challenge focuses on automatic data annotation under long-context settings using Large Language Models (LLMs). The competition is built upon the Qwen3-4B model and adopts the In-context Learning (ICL) paradigm to investigate scalable and high-quality automated annotation methods.
-
-Participating teams are required to use the officially provided datasets and design effective ICL-based annotation solutions tailored for ultra-long context scenarios. All submissions will be evaluated on a unified benchmark dataset. The Organizing Committee will conduct standardized evaluations and determine the final rankings based on the official evaluation results.
-
-## Objectives
-
-This challenge takes Large Language Models (LLMs) as the core technical foundation and targets automated data annotation under ultra-long context constraints, aiming to explore novel paradigms that balance annotation efficiency and annotation accuracy. The competition focuses on the following key scientific and engineering challenges:
-
-- 1. Instruction and Prompt Design:
-
-    How can effective model instructions and prompt strategies be designed in ultra-long context scenarios to guide LLMs toward stable and high-quality data annotation?
-- 2. Ultra-Long Context Construction:
-    
-    When the number of available annotation examples significantly exceeds the model’s context capacity, how can information-dense and structurally coherent ultra-long context inputs be constructed for target data annotation?
-- 3. Multi-Turn and Continuous Annotation:
-
-    In automated multi-round dialogue or continuous interaction settings, how can ultra-long contexts be efficiently leveraged to achieve both consistency and scalability in data annotation?
-
-## Challenge Details
-
-- Participating teams are expected to independently design a complete LLM-based automatic data annotation pipeline and validate their approach under a unified dataset and evaluation protocol. Evaluation scores and rankings will be published on a standardized leaderboard.
-
-- In addition to prediction results, teams must submit a technical report and fully reproducible source code in accordance with the competition requirements. The Organizing Committee will reproduce submitted solutions and review the technical design. The final score will be calculated as a weighted combination of prediction performance and technical solution evaluation, with detailed rules specified by the competition.
-
-- Teams are required to submit their technical reports and complete source code to the official OpenSeek GitHub repository designated by the competition.
-
-- For additional details, please refer to [FlagOS platform](https://flagos.io/RaceDetail?id=296fmsd8&lang=en). All competition-related information is subject to the announcements published on the official platform.
+---
 
 ## Quick Start